@inbook{34195, author = {{Hobscheidt, Daniela and Menzefricke, Jörn Steffen and Gabriel, Stefan and Kühn, Arno and Dumitrescu, Roman}}, booktitle = {{Praxishandbuch Robotic Process Automation (RPA)}}, isbn = {{9783658383787}}, publisher = {{Springer Fachmedien Wiesbaden}}, title = {{{Soziotechnische Herausforderungen bei der Einführung von RPA managen}}}, doi = {{10.1007/978-3-658-38379-4_8}}, year = {{2022}}, } @inbook{34193, author = {{Bansmann, Michael and Dumitrescu, Roman and Fechtelpeter, Christian}}, booktitle = {{Gestaltung digitalisierter Arbeitswelten}}, isbn = {{9783662580134}}, issn = {{2523-3637}}, publisher = {{Springer Berlin Heidelberg}}, title = {{{Transfer von Arbeit 4.0-Anwendungsszenarien}}}, doi = {{10.1007/978-3-662-58014-1_3}}, year = {{2022}}, } @inbook{34194, author = {{Brock, Jonathan and von Enzberg, Sebastian and Kühn, Arno and Dumitrescu, Roman}}, booktitle = {{Praxishandbuch Robotic Process Automation (RPA)}}, isbn = {{9783658383787}}, publisher = {{Springer Fachmedien Wiesbaden}}, title = {{{Nutzung von Process Mining in RPA-Projekten}}}, doi = {{10.1007/978-3-658-38379-4_5}}, year = {{2022}}, } @inbook{34292, author = {{Wolters, Dennis and Engels, Gregor}}, booktitle = {{Product-Focused Software Process Improvement}}, editor = {{Taibi, Davide and Kuhrmann, Marco and Mikkonen, Tommi and Klünder, Jil and Abrahamsson, Pekka}}, isbn = {{9783031213878}}, issn = {{0302-9743}}, pages = {{235--242}}, publisher = {{Springer International Publishing}}, title = {{{Towards Situational Process Management for Professional Education Programmes}}}, doi = {{10.1007/978-3-031-21388-5_16}}, volume = {{13709}}, year = {{2022}}, } @inproceedings{29220, abstract = {{Modern services often comprise several components, such as chained virtual network functions, microservices, or machine learning functions. Providing such services requires to decide how often to instantiate each component, where to place these instances in the network, how to chain them and route traffic through them. To overcome limitations of conventional, hardwired heuristics, deep reinforcement learning (DRL) approaches for self-learning network and service management have emerged recently. These model-free DRL approaches are more flexible but typically learn tabula rasa, i.e., disregard existing understanding of networks, services, and their coordination. Instead, we propose FutureCoord, a novel model-based AI approach that leverages existing understanding of networks and services for more efficient and effective coordination without time-intensive training. FutureCoord combines Monte Carlo Tree Search with a stochastic traffic model. This allows FutureCoord to estimate the impact of future incoming traffic and effectively optimize long-term effects, taking fluctuating demand and Quality of Service (QoS) requirements into account. Our extensive evaluation based on real-world network topologies, services, and traffic traces indicates that FutureCoord clearly outperforms state-of-the-art model-free and model-based approaches with up to 51% higher flow success ratios.}}, author = {{Werner, Stefan and Schneider, Stefan Balthasar and Karl, Holger}}, booktitle = {{IEEE/IFIP Network Operations and Management Symposium (NOMS)}}, keywords = {{network management, service management, AI, Monte Carlo Tree Search, model-based, QoS}}, location = {{Budapest}}, publisher = {{IEEE}}, title = {{{Use What You Know: Network and Service Coordination Beyond Certainty}}}, year = {{2022}}, } @article{27776, author = {{Koldewey, Christian and Rasor, Anja and Reinhold, Jannik and Gausemeier, Jürgen and Dumitrescu, Roman and Chohan, Nadia and Frank, Maximilian}}, issn = {{0040-1625}}, journal = {{Technological Forecasting and Social Change}}, publisher = {{Elsevier}}, title = {{{Aligning strategic position, behavior, and structure for smart service businesses in manufacturing}}}, doi = {{10.1016/j.techfore.2021.121329}}, year = {{2022}}, } @unpublished{29541, author = {{Lienen, Christian and Platzner, Marco}}, title = {{{ReconROS Executor: Event-Driven Programming of FPGA-accelerated ROS 2 Applications}}}, year = {{2022}}, } @phdthesis{29763, abstract = {{Modern-day communication has become more and more digital. While this comes with many advantages such as a more efficient economy, it has also created more and more opportunities for various adversaries to manipulate communication or eavesdrop on it. The Snowden revelations in 2013 further highlighted the seriousness of these threats. To protect the communication of people, companies, and states from such threats, we require cryptography with strong security guarantees. Different applications may require different security properties from cryptographic schemes. For most applications, however, so-called adaptive security is considered a reasonable minimal requirement of security. Cryptographic schemes with adaptive security remain secure in the presence of an adversary that can corrupt communication partners to respond to messages of the adversaries choice, while the adversary may choose the messages based on previously observed interactions. While cryptography is associated the most with encryption, this is only one of many primitives that are essential for the security of digital interactions. This thesis presents novel identity-based encryption (IBE) schemes and verifiable random functions (VRFs) that achieve adaptive security as outlined above. Moreover, the cryptographic schemes presented in this thesis are proven secure in the standard model. That is without making use of idealized models like the random oracle model.}}, author = {{Niehues, David}}, keywords = {{public-key cryptography, lattices, pairings, verifiable random functions, identity-based encryption}}, title = {{{More Efficient Techniques for Adaptively-Secure Cryptography}}}, doi = {{10.25926/rdtq-jw45}}, year = {{2022}}, } @inproceedings{31068, author = {{Chen, Mei-Hua and Mudgal, Garima and Chen, Wei-Fan and Wachsmuth, Henning}}, booktitle = {{EUROCALL}}, title = {{{Investigating the argumentation structures of EFL learners from diverse language backgrounds}}}, year = {{2022}}, } @article{31479, author = {{Baswana, Surender and Gupta, Shiv and Knollmann, Till}}, issn = {{0178-4617}}, journal = {{Algorithmica}}, keywords = {{Applied Mathematics, Computer Science Applications, General Computer Science}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Mincut Sensitivity Data Structures for the Insertion of an Edge}}}, doi = {{10.1007/s00453-022-00978-0}}, year = {{2022}}, } @inbook{32792, abstract = {{Decision makers in complex business environments have different goals and constraints and therefore require tailored decision support systems (DSS). Following a low-code approach, a tailored DSS can be created by a decision maker as a process-based composition of existing, interoperable decision support services. Data incompatibilities may be introduced during the design or execution of such a process-driven DSS, e.g., when a service always generates or a decision maker selects data which violates a data constraint of a subsequent service. These incompatibilities cause interrupted or erroneous decision processes. In this paper, we contribute an approach which enables the detection of data incompatibilities in process-driven DSS during process design and execution. Our approach utilizes the JSON Schema specification to define service interfaces and associated type constraints which data produced by services or decision makers can be validated against. We demonstrate our approach in the context of decision support for energy network planning using a prototypical open-source implementation.}}, author = {{Kirchhoff, Jonas and Gottschalk, Sebastian and Engels, Gregor}}, booktitle = {{Lecture Notes in Business Information Processing}}, isbn = {{9783031115097}}, issn = {{1865-1348}}, publisher = {{Springer International Publishing}}, title = {{{Detecting Data Incompatibilities in Process-Driven Decision Support Systems}}}, doi = {{10.1007/978-3-031-11510-3_6}}, year = {{2022}}, } @misc{33033, author = {{Fehring, Lukas}}, title = {{{Combined Ranking and Regression Trees for Algorithm Selection}}}, year = {{2022}}, } @unpublished{30867, abstract = {{In online algorithm selection (OAS), instances of an algorithmic problem class are presented to an agent one after another, and the agent has to quickly select a presumably best algorithm from a fixed set of candidate algorithms. For decision problems such as satisfiability (SAT), quality typically refers to the algorithm's runtime. As the latter is known to exhibit a heavy-tail distribution, an algorithm is normally stopped when exceeding a predefined upper time limit. As a consequence, machine learning methods used to optimize an algorithm selection strategy in a data-driven manner need to deal with right-censored samples, a problem that has received little attention in the literature so far. In this work, we revisit multi-armed bandit algorithms for OAS and discuss their capability of dealing with the problem. Moreover, we adapt them towards runtime-oriented losses, allowing for partially censored data while keeping a space- and time-complexity independent of the time horizon. In an extensive experimental evaluation on an adapted version of the ASlib benchmark, we demonstrate that theoretically well-founded methods based on Thompson sampling perform specifically strong and improve in comparison to existing methods.}}, author = {{Tornede, Alexander and Bengs, Viktor and Hüllermeier, Eyke}}, booktitle = {{Proceedings of the 36th AAAI Conference on Artificial Intelligence}}, publisher = {{AAAI}}, title = {{{Machine Learning for Online Algorithm Selection under Censored Feedback}}}, year = {{2022}}, } @unpublished{30865, abstract = {{The problem of selecting an algorithm that appears most suitable for a specific instance of an algorithmic problem class, such as the Boolean satisfiability problem, is called instance-specific algorithm selection. Over the past decade, the problem has received considerable attention, resulting in a number of different methods for algorithm selection. Although most of these methods are based on machine learning, surprisingly little work has been done on meta learning, that is, on taking advantage of the complementarity of existing algorithm selection methods in order to combine them into a single superior algorithm selector. In this paper, we introduce the problem of meta algorithm selection, which essentially asks for the best way to combine a given set of algorithm selectors. We present a general methodological framework for meta algorithm selection as well as several concrete learning methods as instantiations of this framework, essentially combining ideas of meta learning and ensemble learning. In an extensive experimental evaluation, we demonstrate that ensembles of algorithm selectors can significantly outperform single algorithm selectors and have the potential to form the new state of the art in algorithm selection.}}, author = {{Tornede, Alexander and Gehring, Lukas and Tornede, Tanja and Wever, Marcel Dominik and Hüllermeier, Eyke}}, booktitle = {{Machine Learning}}, title = {{{Algorithm Selection on a Meta Level}}}, year = {{2022}}, } @unpublished{33150, abstract = {{In this article, we build on previous work to present an optimization algorithm for nonlinearly constrained multi-objective optimization problems. The algorithm combines a surrogate-assisted derivative-free trust-region approach with the filter method known from single-objective optimization. Instead of the true objective and constraint functions, so-called fully linear models are employed and we show how to deal with the gradient inexactness in the composite step setting, adapted from single-objective optimization as well. Under standard assumptions, we prove convergence of a subset of iterates to a quasi-stationary point and if constraint qualifications hold, then the limit point is also a KKT-point of the multi-objective problem.}}, author = {{Berkemeier, Manuel Bastian and Peitz, Sebastian}}, booktitle = {{arXiv:2208.12094}}, title = {{{Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients}}}, year = {{2022}}, } @inproceedings{33230, author = {{Daymude, Joshua J. and Richa, Andréa W. and Scheideler, Christian}}, booktitle = {{1st Symposium on Algorithmic Foundations of Dynamic Networks, SAND 2022, March 28-30, 2022, Virtual Conference}}, editor = {{Aspnes, James and Michail, Othon}}, pages = {{12:1–12:19}}, publisher = {{Schloss Dagstuhl - Leibniz-Zentrum für Informatik}}, title = {{{Local Mutual Exclusion for Dynamic, Anonymous, Bounded Memory Message Passing Systems}}}, doi = {{10.4230/LIPIcs.SAND.2022.12}}, volume = {{221}}, year = {{2022}}, } @inproceedings{33240, author = {{Götte, Thorsten and Scheideler, Christian}}, booktitle = {{SPAA ’22: 34th ACM Symposium on Parallelism in Algorithms and Architectures, Philadelphia, PA, USA, July 11 - 14, 2022}}, editor = {{Agrawal, Kunal and Lee, I-Ting Angelina}}, pages = {{99–101}}, publisher = {{ACM}}, title = {{{Brief Announcement: The (Limited) Power of Multiple Identities: Asynchronous Byzantine Reliable Broadcast with Improved Resilience through Collusion}}}, doi = {{10.1145/3490148.3538556}}, year = {{2022}}, } @inbook{30941, abstract = {{Decision support systems are crucial in helping decision makers to quickly identify optimal business decisions in increasingly volatile and complex business environments. However, the ideal DSS for one decision maker may not optimally address the requirements for decision support of another decision maker. This is due to differences between decision makers in business goals, regulatory restrictions or availability of resources such as data. By using a suboptimal DSS, decision makers risk implementing suboptimal decision recommendations which endanger the success of their business. This presents DSS developers with the challenge to implement a customizable DSS which can be tailored to the individual requirements for decision support of a single decision maker. In order to address this challenge, we suggest a decision support ecosystem in which DSS developers, decision makers and other domain experts collaborate using a shared platform to provide and combine reusable decision support services into a tailored DSS. The contribution of our paper is twofold: First, we define the concept of a decision support ecosystem with respect to existing digital business ecosystems and discuss expected benefits and challenges. Second, we present a reference architecture for a shared platform supporting the realization of a decision support ecosystem. We demonstrate our contributions in the example application domain of regional energy distribution network planning.}}, author = {{Kirchhoff, Jonas and Weskamp, Christoph and Engels, Gregor}}, booktitle = {{Decision Support Systems XII: Decision Support Addressing Modern Industry, Business, and Societal Needs}}, publisher = {{Springer}}, title = {{{Decision Support Ecosystems: Definition and Platform Architecture}}}, doi = {{10.1007/978-3-031-06530-9_8}}, volume = {{447}}, year = {{2022}}, } @inproceedings{33281, abstract = {{Corporate decision makers have individual requirements for decision support influenced by business goals, regulatory restrictions or access to resources such as data. Ideally, decision makers could quickly create tailored decision support systems (DSS) themselves which optimally address their individual requirements for decision support. Although service-oriented architectures have been proposed for DSS customization, they are primarily targeting trained software developers and cannot immediately be adapted by decision makers or domain experts with little to no software development knowledge. In this paper, we therefore motivate an assisted process-based service composition approach which can be used by non-developers to create tailored DSS. For assistance during service composition, we contribute a meta-model for the formalization of both decision support requirements and functionality of decision support services. Models created according to the meta-model can be used to detect mismatches between a decision maker’s requirements for decision support and services selected in the service composition representing a DSS. Furthermore, the formalizations may even be used for automated service composition given a decision maker’s decision support requirements. We demonstrate the expressiveness of our meta-model in the domain of regional energy distribution network planning.}}, author = {{Kirchhoff, Jonas and Weskamp, Christoph and Engels, Gregor}}, booktitle = {{Human-Centered Software Engineering}}, editor = {{Bernhaupt, Regina and Ardito, Carmelo and Sauer, Stefan}}, isbn = {{978-3-031-14785-2}}, pages = {{150–162}}, publisher = {{Springer International Publishing}}, title = {{{Requirements-Based Composition of Tailored Decision Support Systems}}}, doi = {{10.1007/978-3-031-14785-2_10}}, volume = {{13482}}, year = {{2022}}, } @inbook{29872, author = {{Maack, Marten and Meyer auf der Heide, Friedhelm and Pukrop, Simon}}, booktitle = {{Approximation and Online Algorithms}}, isbn = {{9783030927011}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{Server Cloud Scheduling}}}, doi = {{10.1007/978-3-030-92702-8_10}}, year = {{2022}}, }